Stents may be used for treating coronary arteries that are constricted by plaque to re-establish the blood flow and thus the supply of the cardiac muscle.
A 3D-reconstruction of a blood vessel model may be used for estimating the degree of stenosis as well as for planning a treatment strategy. A 3D-blood-vessel model is developed based on at least two angiographic scenes. The precision of the reconstructed blood vessel model is decisive, since the precision directly affects the evaluation of the stenosis or the section of the vessel. “Stenosis” refers to both a stenosis and a section of a vessel.
Due to the limited number of angiographic pictures, the geometric complexity of the 3D-models may be restricted to elliptical cross-sections that are located on a reconstructed 3D-center-line. The re-construction of the models is based on 2D-segmentations of a vessel contour and the vessel center line. The effects of stenoses on the 2D-contour of the vessel strongly depends on the complexity of the stenoses, but also on the perspective of the single views.
FIGS. 1 to 4 illustrate the problem. A metal phantom is used to model an axisymmetric stenosis 1 and an eccentric stenosis 2. Projection images show significant differences of the contours of both stenosis 1 and 2.
FIGS. 1 to 4 depict, for example, different LAO (left anterior oblique) views. FIG. 1 is a view at LAO=90°, FIG. 2 a view at LAO=60°, FIG. 3 a view at LAO=30° and FIG. 4 a view at LAO=0°. The geometrical contour of the axisymmetric stenosis 1 is equally formed in each of the views of FIGS. 1 to 4. However, the contour of the eccentric stenosis 2 may only be seen in the views of FIGS. 1 and 2, e.g. only the views at LAO=90° and LAO=60° depict a crescent shape at the edge of the vessel 3 (represented by the metal phantom). Since the eccentric stenosis does not cover the complete circumference of the vessel, the eccentric stenosis is almost completely hidden in unfavorable views like LAO=0° and LAO=30° (compare FIGS. 3 and 4). The views do not allow a contour based reconstruction of the eccentric stenosis 2.
One solution of the problem is to take additional pictures at further angles that provide important information for improving the 3D-model. Such additional pictures, however, have the disadvantage that the additional pictures increase the time and consequently the costs for acquisition and processing of the pictures. Additionally, the dose of radiation increases for the patient and the cardiologist. Therefore, additional pictures may only be taken if the complexity of the stenosis requires the additional pictures.
If the stenosis is diagnosed directly from 2D-angiography data, the number of pictures and corresponding views are manually assessed by the physician. Furthermore, there is no automated solution for the evaluation of the complexity and the reconstructability of the stenosis based on the 2D-angiography data.
The article of Itu, Lucian, et al.: “A Machine Learning Approach for Computation of Fractional Flow Reserve from Coronary Computed Tomography”, Journal of Applied Physiology, Apr. 14, 2016, presents a machine learning based model for predicting FFR (fractural flow reserve) as an alternative physician-based approach. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physician-based model. The trained model predicts FFR at each point along the center line of the coronary tree and its performance was assessed by comparing the predictions against physician-based computations.